Early geometric deformable model only considered the geometrical nature of the human body.
早期的几何形变模型仅仅反映了人体形变的几何特征。
There are two types of deformable models: parametric deformable model and geometric deformable model.
变形模型分为参数变形模型和几何变形模型两种。
This paper do some research on the principle of the geometric deformable model level set, and introduce the narrow band method.
本文首先对几何变形模型和水平集的相关理论进行了研究，并针对水平集改进算法-窄带法的应用进行了探讨。
Then a new method merging area information and edge information, geometric deformable model with color and intensity priors for medical image segmentation, is proposed.
在此基础上提出了一种混合区域信息和边界信息的方法——基于融合颜色和强度先验信息的几何可变模型的医学图像分割算法。
This thesis investigates and then modifies the existing geometric deformable model by integrating area and edge information and satisfactory result has been successfully reached.
作者从融合图像区域信息和边界信息的角度，对现有几何可变模型进行了改进，取得了比较理想的研究结果。
This paper presents a machine learning method to select best geometric features for deformable brain registration for each brain location.
针对基于属性向量的非线性配准算法，提出用机器学习的方法寻找脑图像中各个点上的最优几何特征向量。
This paper presents a machine learning method to select best geometric features for deformable brain registration for each brain location.
针对基于属性向量的非线性配准算法，提出用机器学习的方法寻找脑图像中各个点上的最优几何特征向量。
应用推荐